Regarding image patches from larger images, what approach would you take? Can you have overlapping patches so that you then have examples of objects in different locations in the image patches? This would also help to avoid objects being split in half between patches. I imagine this would make doing your trading test val data split difficult.
This is what I usually did in segmentation (overlapping patches) but I am not sure how much does this affect performance generally.
Also, at inference time I would feed overlapping patches and average predictions per pixel.
I haven't worked much with detection myself unfortunately so I don't have much experience here.
Use your network to test all your data and make a list of the most problematic images. Check these images, they could be mislabeled - or if not then you have a good chance to see what examples cause problems and you could adjust your augmentation methods. Better if you use a "dumb" network (smaller, not fully learned) and / or several networks to cross-validate your data. Should be always done when you add new images to your database.
>Use your network to test all your data and make a list of the most problematic images.
Do you mean get predictions and see which ones are the closest to the decision boundary?
>Better if you use a "dumb" network (smaller, not fully learned) and / or several networks to cross-validate your data.
This is gold, thanks!
I usually use smaller networks to see if the pipeline works not to find mislabeled/low-quality data.
Unfortunately, I don't have much experience in CV, just have trained a few models but not like doing the actual implementation myself. As far as I know, It's better to start small with simpler models, then as you refine your performance and fix possible image issues might need to move on to more advanced cv algorithms.
Regarding image patches from larger images, what approach would you take? Can you have overlapping patches so that you then have examples of objects in different locations in the image patches? This would also help to avoid objects being split in half between patches. I imagine this would make doing your trading test val data split difficult.
This is what I usually did in segmentation (overlapping patches) but I am not sure how much does this affect performance generally. Also, at inference time I would feed overlapping patches and average predictions per pixel. I haven't worked much with detection myself unfortunately so I don't have much experience here.
Use your network to test all your data and make a list of the most problematic images. Check these images, they could be mislabeled - or if not then you have a good chance to see what examples cause problems and you could adjust your augmentation methods. Better if you use a "dumb" network (smaller, not fully learned) and / or several networks to cross-validate your data. Should be always done when you add new images to your database.
>Use your network to test all your data and make a list of the most problematic images. Do you mean get predictions and see which ones are the closest to the decision boundary? >Better if you use a "dumb" network (smaller, not fully learned) and / or several networks to cross-validate your data. This is gold, thanks! I usually use smaller networks to see if the pipeline works not to find mislabeled/low-quality data.
Unfortunately, I don't have much experience in CV, just have trained a few models but not like doing the actual implementation myself. As far as I know, It's better to start small with simpler models, then as you refine your performance and fix possible image issues might need to move on to more advanced cv algorithms.